Cognitive systems are able to learn and reason in a manner that facilitates their natural and fruitful interaction with humans. Ultimately, this interaction aims to extend and enhance human cognition, not by having cognitive systems operate as subsidiary workers that solve problems for humans, but by having cognitive systems act as expert assistants able to collaborate with humans and provide them with advice in a form compatible with how humans naturally process and understand information.

Knowledge acquisition is central to the design of such cognitive systems. Knowledge should be representable in a form understandable by humans, e.g., as simple arguments represented in high-level symbolic or statistical expressions. At the same time, the process of acquisition itself should exhibit characteristics akin to those of human learning, so that humans can relate to it and be able to interact with it as advice coming from a knowledgeable colleague. Thus, we mean “cognitive” in the workshop’s title to be interpreted as characterizing both the form of “knowledge” and the process of “acquisition”.

Unlike the significant body of work on mining the web for facts or answers to specific questions (e.g., NELL, IBM’s Watson system for Jeopardy!), the workshop’s emphasis is on the acquisition of general inference rules that can be applied by a cognitive system in novel situations to elaborate what has been sensed with plausible and useful inferences. Along with computational efficiency, scalability, autonomy, and formal analysis of the process, key is also the use of naturalistic algorithms. We are more interested in contributions that propose acquisition processes that could potentially err more (when typical humans would also err), but are simple and intuitive, rather than acquisition processes that employ heavy computational machinery to improve performance at the expense of psychological validity.

Since knowledge acquisition cannot proceed independently of other aspects of cognition, such as perception and reasoning / decision making, we also welcome contributions on other aspects of cognition, as long as they are directly tied to knowledge acquisition within a unified framework. We particularly encourage the demonstration of (prototype) cognitive systems that implement the proposed frameworks, and discuss solutions to pragmatic concerns that had to be addressed.

We welcome ongoing and exciting preliminary work. Topics of interest include, but are not limited to:

A number of travel grants (for students and early-stage researchers) to partially subsidize participation in the workshop will be available through a sponsorship from the Artificial Intelligence journal.